



"""simple docstring"""






from collections import defaultdict






def 			snake_case_    ( A_   :						str,							A_   :						str		):


       '''simple docstring'''





       _lowerCamelCase		:							List[str]            =					first_str.lower().strip()
       _lowerCamelCase		:							List[str]            =					second_str.lower().strip()

       # Remove whitespace
       _lowerCamelCase		:							str            =					first_str.replace(''' ''',							''''''		)
       _lowerCamelCase		:							Dict            =					second_str.replace(''' ''',							''''''		)

       # Strings of different lengths are not anagrams
       if len(A_		) != len(A_		):
              return False

       # Default values for count should be 0
       _lowerCamelCase		:							defaultdict[str, int]            =					defaultdict(A_		)

       # For each character in input strings,
       # increment count in the corresponding
       for i in range(len(A_		)		):
              count[first_str[i]] += 1
              count[second_str[i]] -= 1

       return all(_count == 0 for _count in count.values()		)


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   lowerCAmelCase__              =							input('''Enter the first string ''').strip()
   lowerCAmelCase__              =							input('''Enter the second string ''').strip()

   lowerCAmelCase__              =							check_anagrams(input_a, input_b)
   print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")








'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



